13th International Conference on Membrane Computing - MTA Sztaki

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T. Ahmed, G. DeLancy, A. Paun Fig. 1. Time Series of A without noise Fig. 2. Time Series of B without noise Fig. 3. Time Series of C without noise 74
A case-study on the influence of noise to log-gain principles for flux dynamic discovery Using the log-gain theory requires that we know the initial values of all uxes and at least 1,000 steps of each time-series. [23] With data collected from the noisy time series we constructed, we are easily able to supply this information. For our study, we used both the initial values of the uxes and 10,000 steps of our noisy time series data for the log-gain MetaPlab plugin. Our next step was to take the noisy time series data and import it into a model which contained no ux functions. The above model has had it's ux functions removed and the relevant data replaced with our noisy time series data. Such a model is what would typically be used with the log-gain theory, as the log-gain process should generate ux-dynamics by utilizing the time series data gathered from experimentation and without making use of known ux functions. This model is a prime candidate for being used with the MetaPlab log-gain plugin. With noiseless data this process would be quickly veried; however, for our noisy data additional tests will be required. After importing all noisy data and the initial values for the ux functions, we were ready to begin the log-gain process. We made use of the following set of tuners in using our model with the loggain process. Tuner selection was a simple task as we knew the ux functions before hand (recall that we simply assumed some ux functions). As such, tuner selection was a simple process of determining which reactions were governed by which substances. R 0 = A, B, C R 1 = B,C R 2 = C, A R 3 = B R 4 = C As mentioned above, we made use of the MetaPlab log-gain plugin to apply log-gain theory to our model. This plugin requires the initial values of all uxes and at least 1,000 steps of each time-series. The time series data used is the 10,000 step noisy data we generated. The reaction set was input into the plugin with a good Covering Oset Log Gain Property [10], and a polynomial was then generated through the plugin by selecting an appropriate substance. Descriptions for the polynomials were given at the time of generation. The reactions were set up in the following manner: R0 covered substance A, R3 covered substance B, and R2covered substance C. After all tuners and coverings were input into MetaPlab, we ran the plugin. We also used a linear regression plugin to apply curve tting to the clean noise eect and retrieve the ux functions. This only required that we make use of the ux functions and the initial values of the various time series. The time series can either be imported from external les by the log-gain plugin or computed by using the dynamic computation plugin. The log-gain plugin applied log-gain theory to the noisy time series data, but to better understand our results, we took one last step. We noticed that our 75
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Erzsébet Csuhaj-Varjú Marian Gheo
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Editors Erzsébet Csuhaj-Varjú Dep
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Preface In addition to the texts or
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Contents M.A. Martínez-del-Amor, I
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13th Inter
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(Tissue) P systems with decaying ob
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Page 51 and 52: Turing’s three pioneering initiat
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Page 60 and 61: Yu. Rogozhin this obstacle and to g
Page 62 and 63: Yu. Rogozhin 10. J. Cocke, M. Minsk
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Page 70 and 71: T. Ahmed, G. DeLancy, A. Paun almos
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Page 89 and 90: Asynchronuous and maximally paralle
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A. Alhazov, Yu. Rogozhin - one extr
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B. Aman, G. Ciobanu formalisms is e
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B. Aman, G. Ciobanu membranes appea
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B. Aman, G. Ciobanu • [ ] recep r
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B. Aman, G. Ciobanu Definition 3. A
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B. Aman, G. Ciobanu { w i , for all
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B. Aman, G. Ciobanu The four transi
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B. Aman, G. Ciobanu A state space i
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B. Aman, G. Ciobanu to reiterate th
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On structures and behaviors of spik
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L. Cienciala, L. Ciencialová, M. P
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H. ElGindy, R. Nicolescu, H. Wu pat
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H. ElGindy, R. Nicolescu, H. Wu pro
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H. ElGindy, R. Nicolescu, H. Wu (b)
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H. ElGindy, R. Nicolescu, H. Wu 8 r
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H. ElGindy, R. Nicolescu, H. Wu Pse
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H. ElGindy, R. Nicolescu, H. Wu to
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H. ElGindy, R. Nicolescu, H. Wu ter
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H. ElGindy, R. Nicolescu, H. Wu 4.
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H. ElGindy, R. Nicolescu, H. Wu Tab
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H. ElGindy, R. Nicolescu, H. Wu 6 R
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H. ElGindy, R. Nicolescu, H. Wu (wh
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A formal framework for P systems wi
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A new approach for solving SAT by P
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Maintenance of chronobiological inf
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4 3.5 3 2.5 2 1.5 1 0.5 0 0 50 100
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Using a kernel P system to solve th
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Spiking neural P systems with funct
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L.F. Macías-Ramos, M.J. Pérez-Jim
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M.A. Martínez-del-Amor, I. Pérez-
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Membranes with local environments A
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Membranes with local environments T
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Membranes with local environments -
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Membranes with local environments -
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Membranes with local environments I
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On efficient algorithms for SAT dis
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On efficient algorithms for SAT 2 S
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On efficient algorithms for SAT 2.4
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On efficient algorithms for SAT inc
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On efficient algorithms for SAT •
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On efficient algorithms for SAT Not
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On efficient algorithms for SAT the
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On efficient algorithms for SAT 32.
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A. Obtu̷lowicz - its underlying tr
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A. Obtu̷lowicz 2−ramification
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A. Obtu̷lowicz The correctness of
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A. Obtu̷lowicz The arcs (links) fr
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An analysis of correlative and quan
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Sublinear-space P systems with acti
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Modelling ecological systems with t
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D. Sburlan hence one can gather use
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D. Sburlan represents a 0L system.
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D. Sburlan assuming that M is in a
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D. Sburlan q 1 {t−, T 1 ↓, T 2
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D. Sburlan In case of M, the states
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D. Sburlan We introduced a formal s
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P. Sosík [9, 10], several research
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P. Sosík The rules of a system lik
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P. Sosík 1. The family Π is polyn
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P. Sosík (a) subsequently and recu
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P. Sosík contentFinal = content(l,
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P. Sosík Hence, with the aid of th
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P. Sosík 8. Lakshmanan K. and Rama
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S. Verlan, J. Quiros more relevance
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S. Verlan, J. Quiros For a regular
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S. Verlan, J. Quiros digital FPGA c
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S. Verlan, J. Quiros where k j = N
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S. Verlan, J. Quiros Now let us con
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S. Verlan, J. Quiros We consider a
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S. Verlan, J. Quiros the present co
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S. Verlan, J. Quiros Table 2 gives
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S. Verlan, J. Quiros Using similar
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S. Verlan, J. Quiros 5. M. Maliţa,
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Simplifying Event-B models of P sys
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Author Index Adorna H., 143 Agrigor
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13th International Conference on Membrane Computing - MTA Sztaki

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